class_docstr: UCSDped Dataset class.
  
  Args:
      task (TaskType): Task type, 'classification', 'detection' or 'segmentation'
      root (Path | str): Path to the root of the dataset
      category (str): Sub-category of the dataset, e.g. "UCSDped1" or "UCSDped2"
      transform (A.Compose): Albumentations Compose object describing the transforms that are applied to the inputs.
      split (str | Split | None): Split of the dataset, usually Split.TRAIN or Split.TEST
      clip_length_in_frames (int, optional): Number of video frames in each clip.
      frames_between_clips (int, optional): Number of frames between each consecutive video clip.
      target_frame (VideoTargetFrame): Specifies the target frame in the video clip, used for ground truth retrieval
docstr: Create and assign samples.
docstr_tok: ["Create","and","assign","samples","."]
code: def _setup(self) -> None:
      """Create and assign samples."""
      self.samples = make_ucsd_dataset(self.root_category, self.split)
  
imports: from __future__ import annotations
  import logging
  from pathlib import Path
  from shutil import move
  from typing import Any, Callable
  import albumentations
  import cv2
  import numpy
  import torch
  from pandas import DataFrame
  from torch import Tensor
  from anomalib.data.base import AnomalibVideoDataModule, AnomalibVideoDataset
  from anomalib.data.base.video import VideoTargetFrame
  from anomalib.data.task_type import TaskType
  from anomalib.data.utils import DownloadInfo, InputNormalizationMethod, Split, ValSplitMode, download_and_extract, get_transforms, read_image
  from anomalib.data.utils.video import ClipsIndexer
label_desc: Use image node_id 2 for calling the UCSDpedDataset obj's underlying member method code with example usage: obj._setup() without return types